Instructions to use loubnabnl/santacoder-code-to-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use loubnabnl/santacoder-code-to-text with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="loubnabnl/santacoder-code-to-text", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use loubnabnl/santacoder-code-to-text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "loubnabnl/santacoder-code-to-text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "loubnabnl/santacoder-code-to-text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/loubnabnl/santacoder-code-to-text

SGLang

How to use loubnabnl/santacoder-code-to-text with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "loubnabnl/santacoder-code-to-text" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "loubnabnl/santacoder-code-to-text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "loubnabnl/santacoder-code-to-text" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "loubnabnl/santacoder-code-to-text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use loubnabnl/santacoder-code-to-text with Docker Model Runner:
```
docker model run hf.co/loubnabnl/santacoder-code-to-text
```

santacoder-code-to-text / README.md

loubnabnl HF Staff

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metadata

license: openrail
datasets:
  - codeparrot/github-jupyter-code-to-text
library_name: transformers
tags:
  - code

Santacoder code-to-text

This model is a fine-tuned version of bigcode/santacoder on copdeparrot/gitub-jupyter-code-to-text.

Training procedure

The model was trained on 4 A100 for 3h with the following hyperparameters were used during training on 4 A100:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 800